Concise Computer Vision: An Introduction into Theory and Algorithms (Undergraduate Topics in Computer Science)

Concise Computer Vision: An Introduction into Theory and Algorithms (Undergraduate Topics in Computer Science)

Reinhard Klette

Language: English

Pages: 441

ISBN: 1447163192

Format: PDF / Kindle (mobi) / ePub

Many textbooks on computer vision can be unwieldy and intimidating in their coverage of this extensive discipline. This textbook addresses the need for a concise overview of the fundamentals of this field.

Concise Computer Vision provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter.

Topics and features:

* Provides an introduction to the basic notation and mathematical concepts for describing an image, and the key concepts for mapping an image into an image
* Explains the topologic and geometric basics for analysing image regions and distributions of image values, and discusses identifying patterns in an image
* Introduces optic flow for representing dense motion, and such topics in sparse motion analysis as keypoint detection and descriptor definition, and feature tracking using the Kalman filter
* Describes special approaches for image binarization and segmentation of still images or video frames
* Examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration
* Reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding
* Includes a discussion of stereo matchers, and the phase-congruency model for image features
* Presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests

This concise and easy to read textbook/reference is ideal for an introductory course at third- or fourth-year level in an undergraduate computer science or engineering programme.

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edge-artifacts. 3.Perform the Euclidean distance transform (EDT) for calculating the minimum distances between pixel locations p=(x,y) to those edge pixels; use the signed row components x−x edge of the calculated distances for identifying centres of lanes at places where signs are changing and the distance values about half of the expected lane width. 4.Apply a particle filter for propagating detected lane border pixels bottom-up, row by row, such that we have the most likely pixels as lane

an increasing function. The relative histogram h I (u) corresponds to an estimate of a density function, c I (u) to an estimate of a probability distribution function, and to an estimate of the uniform density function. Linear Scaling Assume that an image I has positive histogram values in a limited interval only. The goal is that all values used in I are spread linearly onto the whole scale from 0 to G max. Let u min=min{I(x,y):(x,y)∈Ω}, u max=max{I(x,y):(x,y)∈Ω}, and (2.3) (2.4) As a

respectively Observation 3.3 K-adjacency creates a planar adjacency graph for a given image and ensures that simple digital curves separate inner and outer regions. Back to the case of binary images: If we assume that “white > black”, then K-adjacency means that we have 8-adjacency for white pixels, and 4-adjacency for black pixels, and “black > white” defines the swapped assignment. 3.1.3 Border Tracing When arriving via a scanline at an object, we like to trace its border such that the

selected N cols ×1 “narrow” windows for obtaining an impression about the distribution of image values. Fig. 1.8 Left: Two selected image rows in the intensity channel (i.e. values (R+G+B)/3) of image SanMiguel shown in Fig. 1.3. Right: Intensity profiles for both selected rows Spatial or Temporal Value Statistics Histograms or intensity profiles are examples for spatial value statistics. For example, intensity profiles for rows 1 to N rows in one image I define a sequence of discrete

book except in Exercises 7.2 and 7.6. There are different ways for defining the curvature for a smooth surface. Gaussian Curvature C.F. Gauss defined the surface curvature at a surface point P by considering “small” surface patches S ε of radius ε>0 centred at P. For S ε , let R ε be the set of all endpoints of unit normals at points Q∈S ε ; the set R ε is a region on the surface of the unit sphere. Now let (7.8) where ???? denotes the area measure. This defines the Gaussian curvature at a

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